Seven Infrastructure Patterns That Keep AI Apps Running at Scale

Software developer Maneshwar, building an AI code review tool called LiveReview, outlines seven architectural patterns critical to running AI applications under heavy user load. The core argument is that a well-tuned AI model means little if the surrounding infrastructure cannot handle thousands of simultaneous requests. Key patterns discussed include deploying an API gateway to manage incoming traffic, applying admission control to cap not just request counts but also token usage and concurrent model calls, and using caching to avoid redundant retrieval and embedding work for repeated queries. The article distinguishes between an API gateway, which governs user traffic into the app, and a model gateway, which manages outbound calls to providers like OpenAI or Anthropic. It emphasizes that production failures at scale typically stem from infrastructure bottlenecks rather than model quality, making these engineering patterns essential for any AI product moving beyond demo stage.